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Clustering Smart City Services: Perceptions, Expectations, Responses

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  • Miltiadis D. Lytras

    (School of Business & Economics, Deree College—The American College of Greece, 153-42 Athens, Greece
    Effat College of Engineering, Effat University, Jeddah P.O. Box 34689, Saudi Arabia)

  • Anna Visvizi

    (School of Business & Economics, Deree College—The American College of Greece, 153-42 Athens, Greece
    Effat College of Business, Effat University, Jeddah P.O. Box 34689, Saudi Arabia)

  • Akila Sarirete

    (Effat College of Engineering, Effat University, Jeddah P.O. Box 34689, Saudi Arabia)

Abstract

Smart cities research evolved into one of the most vibrant fields of research and policy-making with sustainability and well-being becoming the bons mots of the debate. The business sector, i.e., the developers and the vendors, form an equally important group of stakeholders in this context. The question is to what extent that debate yields the kind of output that the end-users would expect and would consider useful and usable. A plethora of smart city services exists. Literature suggests that a myriad of new ICT-enhanced tools could find application in urban space. Methodologically speaking, the question is how to link these two meaningfully. The objective of this paper is to address this issue. To this end, smart city services are mapped and clusters of services are identified; end users’ perceptions and expectations are identified and observations are drawn. The value added of this paper is threefold: (i) at the conceptual level, it adds new insights in the ‘normative bias of smart cities research’ thesis, (ii) at the empirical level, it typifies smart city services and clusters them, and (iii) it introduces a practical toolkit that policymakers, regulators, and the business sector might employ to query end-users’ perceptions and expectations to effectively respond to citizens’ needs.

Suggested Citation

  • Miltiadis D. Lytras & Anna Visvizi & Akila Sarirete, 2019. "Clustering Smart City Services: Perceptions, Expectations, Responses," Sustainability, MDPI, vol. 11(6), pages 1-19, March.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:6:p:1669-:d:215434
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    References listed on IDEAS

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    6. Marcos Nahuel Martínez Stanziani, 2020. "Índices de Ciudades Inteligentes: construcción y análisis de un indicador para la ciudad de Bahía Blanca," Asociación Argentina de Economía Política: Working Papers 4374, Asociación Argentina de Economía Política.
    7. Alessandro Crivellari & Euro Beinat, 2020. "LSTM-Based Deep Learning Model for Predicting Individual Mobility Traces of Short-Term Foreign Tourists," Sustainability, MDPI, vol. 12(1), pages 1-18, January.
    8. Radosław Malik & Anna Visvizi & Orlando Troisi & Mara Grimaldi, 2022. "Smart Services in Smart Cities: Insights from Science Mapping Analysis," Sustainability, MDPI, vol. 14(11), pages 1-16, May.
    9. Bernd W. Wirtz & Marcel Becker & Florian W. Schmidt, 2022. "Smart city services: an empirical analysis of citizen preferences," Public Organization Review, Springer, vol. 22(4), pages 1063-1080, December.
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    12. Mona Treude, 2021. "Sustainable Smart City—Opening a Black Box," Sustainability, MDPI, vol. 13(2), pages 1-15, January.
    13. Tu Peng & Xu Yang & Zi Xu & Yu Liang, 2020. "Constructing an Environmental Friendly Low-Carbon-Emission Intelligent Transportation System Based on Big Data and Machine Learning Methods," Sustainability, MDPI, vol. 12(19), pages 1-19, October.

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